Fooling Image Recognition, Electronics Text, Zero-Knowledge Proofs, and Massively Parallel Protein Design
- Robust Adversarial Inputs — tricking deep learning image recognition models. We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives.
- CircuitLab Textbook — free introductory electronics textbook, work in progress.
- The Hunting of the Snark — a treasure hunt consisting of cryptographic challenges that will guide you through a zero-knowledge proof (ZKP) learning experience. As a reminder, zero-knowledge proofs, invented decades ago, allow verifiers to validate a computation on private data by allowing a prover to generate a cryptographic proof that asserts to the correctness of the computed output.
- Massively Parallel Protein Design — We combined computational protein design, next-generation gene synthesis, and a high-throughput protease susceptibility assay to measure folding and stability for more than 15,000 de novo designed miniproteins, 1,000 natural proteins, 10,000 point mutants, and 30,000 negative control sequences. This analysis identified more than 2,500 stable designed proteins in four basic folds—a number sufficient to enable us to systematically examine how sequence determines folding and stability in uncharted protein space. Clever approach to understanding protein folding. (via Ian Haydon )
Continue reading Four short links: 18 July 2017.